We evaluate our model on two widely-used public benchmarks: the V-COCO (Verbs in COCO) [1]} and HICO-DET [2]} datasets.
V-COCO is a subset of COCO composed of 5,400 trainval images and 4,946 test images.
For V-COCO dataset, we report the \(\text{AP}_{\text{role}}\)  over 25 interactions in two scenarios.
In Scenario 1 (denoted as \(\text{AP}_{\text{role}}^{\#1}\) ), detectors should predict an output indicating the non-existence of an object ([0,0,0,0]) when the target object is occluded, while in Scenario 2 (denoted as \(\text{AP}_{\text{role}}^{\#2}\) ), only the localization of human and interaction classification is scored for such cases.
HICO-DET contains 37,536 and 9,515 images for each training and test splits with annotations for 600 \(\langle verb, object \rangle \)  interaction types.
In HICO-DET dataset, there are two different evaluation settings: Default and Known object.
The former measures AP on all the test images, while the latter only considers the images with the object class corresponding to each AP.
We report our score with both settings.
Note that the Default is a more challenging setting as we also need to distinguish background images.
We follow the previous settings and report the mAP over three different category sets: (1) all 600 HOI categories in HICO (Full), (2) 138 HOI categories with less than 10 training instances (Rare), and (3) 462 HOI categories with 10 or more training instances (Non-Rare).
